Quantitative comparisons of satellite observations and cloud models
Date
2011
Authors
Wang, Fang, author
Kummerow, Christian D., advisor
Vonder Haar, Thomas H., committee member
Cotton, William R., committee member
Ramirez, Jorge A., committee member
Journal Title
Journal ISSN
Volume Title
Abstract
Microwave radiation interacts directly with precipitating particles and can therefore be used to compare microphysical properties found in models with those found in nature. Lower frequencies (< 37 GHz) can detect the emission signals from the raining clouds over radiometrically cold ocean surfaces while higher frequencies (≥ 37 GHz) are more sensitive to the scattering of the precipitating-sized ice particles in the convective storms over high-emissivity land, which lend them particular capabilities for different applications. Both are explored with a different scenario for each case: a comparison of two rainfall retrievals over ocean and a comparison of a cloud model simulation to satellite observations over land. Both the Goddard Profiling algorithm (GPROF) and European Centre for Medium-Range Weather Forecasts (ECMWF) one-dimensional + four-dimensional variational analysis (1D+4D-Var) rainfall retrievals are inversion algorithms based on the Bayes' theorem. Differences stem primarily from the a-priori information. GPROF uses an observationally generated a-priori database while ECMWF 1D-Var uses the model forecast First Guess (FG) fields. The relative similarity in the two approaches means that comparisons can shed light on the differences that are produced by the a-priori information. Case studies have found that differences can be classified into four categories based upon the agreement in the brightness temperatures (Tbs) and in the microphysical properties of Cloud Water Path (CWP) and Rain Water Path (RWP) space. We found a category of special interest in which both retrievals converge to similar Tb through minimization procedures but produce different CWP and RWP. The similarity in Tb can be attributed to comparable Total Water Path (TWP) between the two retrievals while the disagreement in the microphysics is caused by their different degrees of constraint of the cloud/rain ratio by the observations. This situation occurs frequently and takes up 46.9% in the one month 1D-Var retrievals examined. To attain better constrained cloud/rain ratios and improved retrieval quality, this study suggests the implementation of higher microwave frequency channels in the 1D-Var algorithm. Cloud Resolving Models (CRMs) offer an important pathway to interpret satellite observations of microphysical properties of storms. High frequency microwave brightness temperatures (Tbs) respond to precipitating-sized ice particles and can, therefore, be compared with simulated Tbs at the same frequencies. By clustering the Tb vectors at these frequencies, the scene can be classified into distinct microphysical regimes, in other words, cloud types. The properties for each cloud type in the simulated scene are compared to those in the observation scene to identify the discrepancies in microphysics within that cloud type. A convective storm over the Amazon observed by the Tropical Rainfall Measuring Mission (TRMM) is simulated using the Regional Atmospheric Modeling System (RAMS) in a semi-ideal setting, and four regimes are defined within the scene using cluster analysis: the 'clear sky/thin cirrus' cluster, the 'cloudy' cluster, the 'stratiform anvil' cluster and the 'convective' cluster. The relationship between Tb difference of 37 and 85 GHz and Tb at 85 GHz is found to contain important information of microphysical properties such as hydrometeor species and size distributions. Cluster-by-cluster comparison between the observations and the simulations discloses biases in the model including overproduction of supercooled water and large hail particles. The detected biases shed light on how the model should be adjusted to generate more realistic microphysical relationships for each cluster. Guided by the model/observation discrepancies in the 'convective' cloud cluster, a new simulation is performed to provide dynamic adjustments by generating more but smaller hail particles.
Description
Rights Access
Subject
cloud microphysics
rainfall retrieval
microwave remote sensing